“The next mass shooting will take place on February 12, 2014, in Spokane, Washington. It will be committed by an emotionally disturbed, 38 year-old white man who will kill seven people and wound six more at a place he used to work using a semi-automatic handgun he purchased legally in the state.”

The piece uses data from the Mother Jones database of mass shootings and some probability to make these predictions (and it does use the word “predictions”). Local officials around here were quick to point out that the analysis is “without validity” and today the author, Philip Bump, wrote a follow-up that starts out as an apology to Spokane and then doubles down on how scared we should all be:

“Spokane, you're not in any more danger next February 12 than we are here in Manhattan or the people who work at the D.C. Navy Yard or people in Cleveland or Tulsa or Fresno or Augusta or Perth Amboy. Which is to say: we are all in danger.”

The original article is absurd, but mostly it’s reckless. It didn’t make me scared for Spokane, it made me angry that someone wrote fiction in the guise of statistics and data journalism.

One main point of the piece is a useful and good one: We should expect another mass shooting in America. We should not be afraid to talk about that.

Aside from that, the piece is both sensational and bleak without offering a course of action. It says: This isn’t true, but here’s what will happen. Don’t really be scared, but be scared. It offers no alternative outcome.

The value of data is learning from it. Good data sets help us understand how things are, and how to make better decisions about the future. Poverty data tells us whether the social safety net is working, and then helps us craft improvements. Education data tells us whether we’re doing a good job at teaching our youth, which lets us have a more informed debate about how to do it better. Polling data tells campaigns whether their message and dollars are working the way they expect. Military suicide data helps the military better understand risk factors and create programs intended to help. Meteorologists present weather data as forecasts so we know whether to bring a jacket tomorrow. We use data to predict things all the time.

But as far as the future goes, data is only a starting point. The Atlantic piece let data tell the whole story, like some twisted Ghost of Christmas Yet To Come, but without the waking up to Christmas morning. In this narrative, there’s no opportunity to make changes. Indeed, the final line in today’s update says that “we can try to figure out the pattern and try to break it.” But cobbling together various statistics doesn’t make a meaningful pattern.

The Mother Jonesdata set used in this prediction has been criticized for being too narrowly focused, but it still can tell us valuable things that let us ask more informed questions. Instead of making wild predictions, here are some of the questions we could raise from this data:

- Why are the shooters most often white? Is this changing as our our nation’s demographics change? Does this vary with age or location?

- If there’s a correlation with mental illness, what can we do about that? Are these people who sought treatment? Were there prior concerns? Where did the system break down in each instance?

- If most of the guns used were legally purchased in state, is there any differences when you factor in varying state laws? Is it likely that new laws would have an effect? Did the shooters attempt to get other weapons?

- If Washington state has an unusually high number of mass shootings for its population, why is that? Has anything been done after previous shootings? Did we make any changes as a result?

- If the average death toll was 7.6, but it takes four deaths for a shooting to even qualify for the data set, what other mass shooting attempts have been stopped or thwarted? What happened in those instances and what can we learn from them?

A lot of the other factors they use just show what a futile exercise it is to make predictions. Age, time of year, location — shootings have covered the whole range. But they are also very rare. It would take a much larger data set to make meaningful predictions, and hopefully that’s not where we’re headed.